CaliPro : A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Com
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Cellular and Molecular Bioengineering ( 2020) https://doi.org/10.1007/s12195-020-00650-z
Original Article
CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models LOUIS R. JOSLYN
,1,2 DENISE E. KIRSCHNER,2 and JENNIFER J. LINDERMAN1
1
Department of Chemical Engineering, University of Michigan, G045W NCRC B28, 2800 Plymouth Rd, Ann Arbor, MI 481092136, USA; and 2Department of Microbiology and Immunology, University of Michigan Medical School, 1150 W Medical Center Drive, 5641 Medical Science II, Ann Arbor, MI 48109-5620, USA (Received 13 April 2020; accepted 2 September 2020) Associate Editor Michael R. King oversaw the review of this article.
Abstract Introduction—Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short. Methods—Herein, we develop CaliPro, an iterative, modelagnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro. Results—We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals. Conclusions—We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may
Address correspondence to Jennifer J. Linderman, Department of Chemical Engineering, University of Michigan, G045W NCRC B28, 2800 Plymouth Rd, Ann Arbor, MI 48109-2136, USA; Denise E. Kirschner, Department of Microbiology and Immunology, University of Michigan Medical School, 1150 W Medical Center Drive, 5641 Medical Science II, Ann Arbor, MI 48109-5620, USA. Electronic mails: [email protected], [email protected]
be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis. Keywords—Mathematical modeling, Parameter estimation, Highest density region, Alternative de
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